Solving the Price-Setting Newsvendor Problem with Parametric Operational Data Analytics (ODA)

Abstract

We study the data-integrated price-setting newsvendor problem in which the price-demand relationship is described by some parametric model with unknown parameters. We formulate the operational data analytics (ODA) framework that features a data-integration model and a validation model. The data-integration model consists of a class of functions, called the operational statistics. Each operational statistics maps the available data to the ordering decision. The validation model finds, among the set of candidate operational statistics, the ordering decision that leads to the highest actual profit, which is unknown due to the unknown demand parameters. This ODA framework leads to a consistent estimate of the profit function, with which we optimize the pricing decision. The derived quantity and price decisions demonstrate robust profit performance even when the sample size is very small in relation to the demand variability. Compared with the conventional approach with which the unknown parameters are estimated and then the decisions are optimized, the ODA framework produces significantly superior performance in the mean, standard deviation, and minimum of the profit, suggesting the robustness of the ODA solution especially in the small-sample regime.

Keywords: Price-Setting Newsvendor Problem, Data-Integration Model, Operational Statistics, Small Sample Size